East Asian summer rainfall stimulated by subseasonal Indian monsoonal heating

The responses of the East Asian summer monsoon (EASM) to the Indian summer monsoon (ISM) have been the subject of extensive investigation. Nevertheless, it remains uncertain whether the ISM can serve as a predictor for the EASM. Here, on the basis of both observations and a large-ensemble climate model experiment, we show that the subseasonal variability of abnormal diabatic heating over India enhances precipitation over central East China, the Korean Peninsula, and southern Japan in June. ISM heating triggers Rossby wave propagation along the subtropical jet, promoting southerly winds over East Asia. The southerly winds helps steer anomalous mid-tropospheric warm advection and lower-tropospheric moisture advection toward East Asia, providing conditions preferential for rainband formation. Cluster analysis shows that, depending on jet structures, ISM heating can serve as a trigger as well as a reinforcer of the rainband.

The presentation is illogical. Section 2.1 (dominant mode of June East Asian precipitation and its controlling factor) presented three figures (Figs. 1, 2, and 3). They all show interannual variations of the June precipitation anomaly, associated water vapor fluxes, and diabatic heat sources. These results are well-known in many previous works and are nothing new. Besides, the three figures' results have nothing to do with the central theme (subseasonal variation). What caused the interrelated variations was never discussed. Yet, the authors jumped to conclude, "The potential role of the ISM on the dipole precipitation pattern over East Asia is confirmed in the monthly mean field'. (Line 129-130).
The citation of the literature in the introduction is ad hoc and does not acknowledge original contributions, reflecting an insufficient depth of the Authors' understanding and knowledge relevant to their study.
There are many other issues. However, the above concern makes me feel negative about the work. I recommend the rejection of the manuscript for publication.

Reviewer #2 (Remarks to the Author):
This is an interesting analysis with a primary emphasis on the role of the CGT in linking the ISM and ESAM on subseasonal time scales. According to their analysis and results, the authors suggested that diabatic heating anoamlies associated with the ISM is critical to drive a regional component of the CGT over East Asia in June and synchronize rainfall variability along the path of the CGT from central India to East Asia. Actually, this physical connection and the role of the ISM in the formation of the CGT have been known for decades. The novelty of this new study primarily lies in its use of the K-mean clustering analysis and a large-ensemble simulation to reveal the connection of the two submonsoon systems over shorter time scales. However, a number of minor and major issues that are related to technical and scientific components of the study remain unclear and thus need to be addressed or clarified first before the manuscript can be considered for publication in the journal. Although the points I raised below regard many fundamental aspects of the formation of the CGT, I still feel that the study has strong potential to become an important piece of work that will greatly improve our physical understanding of the CGT.
1. The CGT is the leading NH circulation mode prevailing from June to September. The current study only focused on its variability in June. It is not clear to me how to apply what we learn from this month's situation to variations over other months.
2. In Fig1, the authors calculated the correlation of the blue and black curves and added some relevant discussion on this poor relationship. I don't fully understand why the authors took some effort to do this calculation since it is not expected to see a high correlation of these two time series given that these two curves are generated in very different climate scenarios.
3.It is still not clear to me what we can learn from the clustering analysis. If I understand the method right, these four modes are not orthogonal to each other and there are some common features shared by these four modes. If so (they look very similar in Fig. 6 &7), I feel the author should put more attention on these common features and also distinctions of each mode from the common features.
4.The study emphasized that the CGT mode presented here varies energetically on subseasonal time scales, using a lead-lag composite method covering data from day-10 to day+10. In the paper, it is better to further illustrate what the main periodicity of this feature is. A power spectrum analysis of the time series related to this subseasonal variability may help the authors answer this question. 5. Some minor wording issues pop up here or there in the current form. I prefer to place my main focus on these wording problems next round after these scientific issues I just raised could be ironed out.

Comment on "East Asian summer rainfall stimulated by subseasonal Indian monsoonal heating" by Li et al.
The authors found that on the sub-seasonal timescales, an Indian anomalous diabatic heating may serve as a predictor for East Asian rainfall in early summer. The authors first identified the leading mode of June rainfall in East Asia on the interannual timescales and revealed the relation to Indian rainfall. Then, the authors proposed that the Indian diabatic heating anomaly can lead to East Asian rainfall variability through a wave train along the Asian westerly jet. Finally, the authors showed the different role of the Indian diabatic heating, depending on jet structures.
The authors used a large ensemble simulations to support the results revealed based on the observations. The large number of samples made the result more robust and reliable. The results are interesting and may help to improve understanding and prediction of sub-seasonal variability of East Asian summer rainfall. However, I have a major concern about the logic connection between the three parts in the Results section. First, I am a little confused about the connection between the results in sections 2.1 and 2.2. In section 2.1, the authors found a close relationship, on the interannual timescales, between the leading mode of East Asian rainfall in June and Indian rainfall. In section 2.2, the authors attempted to explain the interannual connection using the sub-seasonal variability of Indian diabatic heating, which is, however, not convincing. As described in Lines 151-152, for example, the dipole rainfall pattern in East Asia, which is the leading mode in June on the interannual timescales but is not prominent on the sub-seasonal timescales. Second, in section 2.3, the authors tried to analyze the interaction of CGT and wet band in East Asia using cluster analysis based on jet structure, and proposed the Indian diabatic heating anomaly can serve as a trigger or a reinforcer for wet band depending on if the warm advection is preexisting. What is the relationship between the jet structure and the preexisted warm advection? How does the jet structure affect different wave propagation illustrated in Figure 7, and how does the jet structure determine if the Indian diabatic heating is a trigger or a reinforcer for wet band in East Asia? The result of the different role of Indian diabatic heating, depending on jet structure, in wet band in East Asia is interesting, but the mechanisms need further clarification.

Minor comments
In section 2.2, the authors used the Q1 to represent the Indian summer monsoon heating. Why not use rainfall, which is more direct and maybe more accurate because the Q1 is calculated as the residual based on the thermodynamic equation?
What process is related to the dry band in East Asia since it is not correlated with the CGT wave pattern, Indian diabatic heating, and even rainfall in the western North Pacific (Fig. 6)? In addition, what is the relationship between the wet and dry band?
The authors showed that the dipole rainfall pattern in East Asia can exist without Indian diabatic heating (Fig. S6). So, how much can the Indian diabatic heating account for the dipole rainfall pattern variability? Figure 6d: the title should be "V/T500" L195: How does the anticyclonic anomaly cause the warm advection? Which component plays a dominant role, for example, the anomalous temperature advected by the climatological zonal/meridional winds or the climatological temperature advected by the anomalous zonal/meridional winds? L206-208: what do you mean "the jet stream, which can also drive mechanically induced temperature advection that influences precipitation over East Asia"? Is the jet-drive mechanism different from the warm advection due to the anticyclone? If yes, what is the jet-drive mechanism?
Reviewer #1 (Remarks to the Author): The authors attempt to find out whether the Indian summer monsoon (ISM) can serve as a predictor of the East Asian summer monsoon (EASM) on the subseasonal time scale in June. They argue that Indian subseasonal heating in June could enhance the East Asian rain band by triggering Rossby wave propagation along the subtropical jet and generating an anticyclone over the EA, which strengthens mid-tropospheric warm advection and lower-tropospheric moisture advection conducive to the EA rainband formation. Response: We appreciate the time and effort of the reviewer who has put into careful reading and consideration of our manuscript, and we are grateful for the thoughtful comments and suggestions. The comments have significantly helped us to improve our manuscript and provide opportunity to see our research from a different perspective. The following is our point-to-point reply.
Overall assessment The main conclusion of this work is flawed. The authors claimed that the Indian subseasonal heating in June is the critical factor controlling the dipole precipitation over EA. The authors presented Fig. 4 to support their claim. However, Figure 4 indicates the opposite: EA rainfall occurs before the ISM heating peak, … Response: As the reviewer pointed out, EA rainfall (wet band) can occur even before ISM heating maximum (lag0). This character is more evident in the HIST (Fig. S4 in the original manuscript, and Fig. S2 in the revised manuscript) than that in observation, as we have discussed in lines 245-249 in the original manuscript. This was presumably owing to the selected huge samples in HIST, totally 2,757 samples which includes cases that have EA wet band exist prior than lag0. Our Kmeans clustering classified some cases such as cluster 1 and 4 ( Fig. 6 and Fig. 7 in the original manuscript; Fig. 3 and Fig. 4 in the revised manuscript) in which forcers other than ISM heating could induce EA wet band variability in the lead days (prior than lag0). These clusters showed that ISM heating can act as reinforcer of the rain band (line 218-222 in the original manuscript). We have revised the manuscript for better explanation of these findings (line 90-92 and line 250-254 in the revised manuscript).
… and when ISM heating decays, the EASM rainfall intensifies, meaning that ISM heating is not forcing EA rainfall. Response: Thank you for your comments. After lag0 day (i.e., after the ISM heating maximum), the precipitation intensity is enhanced in all clusters (Fig. 6 in the original manuscript, or Fig. R1-2c for convenient). Therefore, the ISM heating is potentially to play a role in the modulation of EA wet band.
For better illustration the role of ISM heating on EA rainfall, we deepen the analysis to bridge them by considering CGT (circumglobal teleconnection). Here, a reference CGT pattern was created by applying the EOF analysis to seasonal mean (June-September) meridional wind at 200-hPa (V200) over the domain of 20°-60°N, 0°-150°E, following the previous study (Yasui and Watanabe, 2010). We confirmed the EOF1 patterns from JRA-55 and HIST resemble each other and could represent CGT-like wave train; therefore, for convenience we used the HIST-based EOF1 pattern in the following figures (grey contours in Fig. R1-1; CGTI-PC1).
On lead 10 day, the CGT wave train propagates from Western Europe towards the ISM domain. At this stage, the East Asian CGT wave train is rather faint (Figs. R1-1a and b). On lag0 day, the vigorous ISM diabatic heating forces a wave train and dominates over East Asia (Figs. R1-1c and d), inducing a southerly wind that is prominent there. A few days later, the CGT emerges again but V200 anomalies are more intensive in the East Asian region, where the southerly wind still dominates. The southerly wind is important for EA rainfall, as we proposed in Fig. R1-2. The prominent southerly winds (Figs. R1-1 and R1-2e) promote the midtropospheric warm advection (color shading in Fig. R1-2d), as well as low-level moisture supply ( Fig. R1-2f) to the EA, providing the preferential conditions for the wet band to form. We also confirmed these features of lag0 and lag10 are presented in each cluster but with slight difference in wavelength (figures omitted), in consistent with Fig. S5  We set the resolution of LBM to be T42L20 (i.e., 128 × 64 grids horizontally and 20 sigma levels vertically). The June basic state was used from the climatological values of JRA-55 from 1979 to 2020. The observed ISM diabatic heating of the June lag0 days averaged anomalies was imposed over the 10°-30°N, 70°-85°E region after regridding from JRA-55 to T42L20 resolution ( Fig.  R1-3). The LBM output well overlaps the wave train at lag10 (Fig. R1-1f, red contours), suggesting the wave train induced by diabatic heating of ISM is developed and trapped in the westerlies and propagates in phase with CGT. We trust these discussions, added to line 112-146 in the revised manuscript, can enrich the value of our findings.  at lag0 days, (b) lag 3-4 days precipitation (shading) and 200-hPa geopotential height (contours), and temporal evolution from lead 10 day to lag 10 day of zonal mean (c) precipitation (shading) and 500-hPa temperature advection (contours, unit: K/day), (d) 500-hPa horizontal temperature advection (shading) and meridional temperature advection (contours, unit: K/day), (e) 500-hPa meridional wind (shading) and temperature (contours, unit: K), and (f) 850-hPa meridional wind (shading) and vertical integrated water vapor flux (arrows), for clusters 1-4 (columns from left to right, respectively). The zonal mean is the average over 105°-140°E. The stippled regions denote anomalies significant at the 99% confidence level based on the two-sided Student's t test. Green hatching in (d) indicates vertical velocity (unit: Pa/s) lower than 0, significant at the 99% confidence level. "Nhist" indicates the number of samples used for the composite.
In the revised manuscript, Figs. R1-1 and R1-2 was adopted as Figs related variations was never discussed. Yet, the authors jumped to conclude, "The potential role of the ISM on the dipole precipitation pattern over East Asia is confirmed in the monthly mean field'. (Line 129-130).
Response: Thank you for your comments. In the previous version of the manuscript, we aimed to introduce a link between East Asian subseasonal rainfall variability and interannual variability as a theme to guide readers towards seasonal prediction. However, as suggested by the reviewer, we have decided to remove most of the interannual results in the revised manuscript as they are less relevant to the main objective of the study. Figure R1-4 (now Figure 6 in the revised manuscript), confirm that stronger ISM forcing, indicated by higher ISMH values, correspond to higher PC1 scores for East Asian precipitation (EAPR-PC1) at interannual timescale. This is evidence denoting that subseasonal activity of ISM heating could lead to the modulation of interannual precipitation variability over East Asia. Description related to this has been added to discussion section (line 258-290), to underscore the potential significance of subseasonal ISM heating to interannual variation of East Asian rainband.
The subseasonal inter-related variations among CGT, ISM, and EASM have been discussed in the above replies (i.e., Figs. R1-1 to 1-3). We believe these can address the reviewer's concern.
Fig R1-4. Composite June monthly mean precipitation pattern based on observed (a) years with EAPR-PC1 greater than 0.5, (b) years with both EAPR-PC1 greater than 0.5 (EAPR-PC1+) and with detection of lag0 day, (c) years with EAPR-PC1 greater than 0.5 but without lag0 day, and (d) years with lag0 day only. (i) probability (right y-axis) of the interannual EAPR-PC1 (x-axis) distribution for all samples (black curve) and samples with lag0 (red curve), where the grey bars represent the EAPR-PC1 score (left y-axis) estimated from the conditioned lag0 ISMH magnitude for each 1K/day interval (x-axis). (e)-(h) and (j) are similar to (a)-(d) and (i), respectively, but for HIST. Dotted regions denote anomalies significant at the 90% and 99% confidence level for the observations and HIST, respectively. "N" indicates the number of samples used for the composite. The projected EAPR CGTI-PC1 values are shown on the top right of (d) and (h) based on Fig. S7 in the revised supporting information.
The citation of the literature in the introduction is ad hoc and does not acknowledge original contributions, reflecting an insufficient depth of the Authors' understanding and knowledge relevant to their study. Response: We have included the following literatures in our revised introduction and acknowledged their contributions in the field related to our study. Listed below is ordered based on the sequence of appearance.
There are many other issues. However, the above concern makes me feel negative about the work. I recommend the rejection of the manuscript for publication. Response: After making extensive revisions of the manuscript considering your valuable comments, we are confident that the quality of the study has significantly improved. We would like to kindly invite the reviewer to spend time to read our revised manuscript again.

Reviewer #2 (Remarks to the Author):
This is an interesting analysis with a primary emphasis on the role of the CGT in linking the ISM and ESAM on subseasonal time scales. According to their analysis and results, the authors suggested that diabatic heating anomalies associated with the ISM is critical to drive a regional component of the CGT over East Asia in June and synchronize rainfall variability along the path of the CGT from central India to East Asia. Actually, this physical connection and the role of the ISM in the formation of the CGT have been known for decades. The novelty of this new study primarily lies in its use of the K-mean clustering analysis and a large-ensemble simulation to reveal the connection of the two submonsoon systems over shorter time scales. However, a number of minor and major issues that are related to technical and scientific components of the study remain unclear and thus need to be addressed or clarified first before the manuscript can be considered for publication in the journal. Although the points I raised below regard many fundamental aspects of the formation of the CGT, I still feel that the study has strong potential to become an important piece of work that will greatly improve our physical understanding of the CGT. Response: We appreciate the reviewer's valuable comments and encouragement. We have incorporated the reviewer's concerns and comments into the revised manuscript. Below is our point-by-point response.
1. The CGT is the leading NH circulation mode prevailing from June to September. The current study only focused on its variability in June. It is not clear to me how to apply what we learn from this month's situation to variations over other months. Response: Thank you for your comments. Your comment motivated us to investigate the subseasonal ISM-CGT-EASM connection in other summer months. To achieve this, we analyzed the months of July to September by applying the same method with June. Here, we used standard deviation of daily ISMH values for each JRA-55 and HIST, considering different threshold value for each month as summarized in Table R2-1. Lead-lag composite analyses were then performed using adjusted thresholds for each month (Figs. R2-1 and R2-2). Our results suggest that, in contrast to June, the ISM diabatic heating during July to September is weakly associated with the CGT circulation pattern at subseasonal time scale. A meridional dipole-like precipitation pattern was found in the July observation, but not in the HIST. Meanwhile, the response of precipitation in August and September is generally much weaker than that in June.  experiments were carried out to see the atmospheric response to the ISM diabatic heating. The LBM is capable of simulating a linear response to a prescribed forcing, by removing nonlinearity of the processes. We set the resolution of LBM to be T42L20 (i.e., 128 × 64 grids horizontally and 20 sigma levels vertically). Horizontal diffusion with an e-folding time scale of 2 hours was applied.
We considered five scenarios (i.e., each month from May to September) with the 1979-2020 JRA-55 monthly mean climatological basic state. For each scenario, the same ISM diabatic heating of the June lag0 days averaged anomalies is imposed over the 10°-30°N, 70°-85°E region, which is regridded from JRA-55 to T42L20 resolution (Fig R2-3).
The LBM output shows that in May and June, the ISM diabatic heating could trigger a regional CGT that propagates toward East Asia with "high-low-high" wave train pattern along the westerlies (Fig. R2-4). In contrast, the wave train does not exist in the LBM simulation for July to September. Furthermore, the East Asian anticyclone as a part of the CGT is shifted westwards from central-eastern China to the western edge of the Tibetan Plateau in simulations for summer months, which is no longer typical CGT patterns.
We have added Figs. R2-3 and R2-4 as Fig. S10 and S3, respectively, and the relevant descriptions to the revised manuscript (line 101-108 and line 390-399). 2. In Fig1, the authors calculated the correlation of the blue and black curves and added some relevant discussion on this poor relationship. I don't fully understand why the authors took some effort to do this calculation since it is not expected to see a high correlation of these two time series given that these two curves are generated in very different climate scenarios.
Response: In the previous version of the manuscript, we aimed to introduce a link between East Asian subseasonal rainfall variability and interannual variability to guide readers towards seasonal prediction. In this revision, we have decided to remove most of the interannual results in the revised manuscript as they are less relevant to the main objective of the study. Instead, an additional analysis was made in the revised manuscript presenting how subseasonal variability can affect interannual precipitation variability. Figure R2-5 (now Figure  6 in the revised manuscript) confirms that stronger ISM forcing, indicated by higher ISMH values, corresponds to higher PC1 scores for East Asian precipitation (EAPR-PC1) at interannual timescale. This is evidence denoting that subseasonal activity of ISM heating could lead to the modulation of interannual precipitation variability over East Asia. Description related to this has been added to the discussion section in the revised manuscript (line 258-290). 3.It is still not clear to me what we can learn from the clustering analysis. If I understand the method right, these four modes are not orthogonal to each other and there are some common features shared by these four modes. If so (they look very similar in Fig. 6 &7), I feel the author should put more attention on these common features and also distinctions of each mode from the common features. Response: Thank you for your interesting comments. Our reply is twofold as below. i. Are clustered jet features orthogonal?
We have created 4 clusters by inputting the values of latitude, longitude, and corresponding horizontal wind speed at 200-hPa along the jet axis into the K-means clustering. As the reviewer pointed out, the K-means clustering does not necessarily ensure that the clustered patterns are orthogonal, as it is a distance-based algorithm. To test the orthogonality of the clustered result, the same jet axes data were input to the PCA analysis. The first two leading modes explained 20.95% and 10.43% of the total variance, respectively. By comparing the result of the K-means clustering and two leading modes of the PCA analysis, we found a clear relationship between these two methods ( Fig. R2-6). The group averages for clusters 1 and 3 correspond well to the negative PC2 and the positive PC2, while the group averages of clusters 2 and 4 are similarly related to the negative and positive PC1. Therefore, mean characteristics of four clusters can be assumed to be orthogonal, although the individual samples may not be.
We have added Fig. R2-6 as Fig. S12 in the supporting information and relevant description to Line 414-416 in the revised manuscript.
ii. What can we learn from the clustering analysis?
Our claim is that the response of downstream atmospheric circulation over East Asia to upstream ISM heating is different according to the pathway of the jet stream. The vigorous ISM diabatic heating at lag0 perturbs the air close to the westerly jet in June and plays a role as Rossby wave source. Since westerly jet stream can act as a waveguide (Hoskins and Ambrizzi, 1993), it could confine the wave and direct it toward East Asia. Our results depict that clusters 2 and 3 show southward bending of the jet over East Asia, hence, the wave train is heading to the south, forming a cold anomaly (negative geopotential height anomaly) center over East Asian sector on lag0 (Figs. R2-7b and c). The cold center is consequently positioned southward of the jet (south of 35°N). In contrast, clusters 1 and 4 represent the jet running straight, and thus, the wave tends to propagate zonally toward East Asia. This condition leads to the position of the cold center to be more north over the East Asian sector (Figs. R2-7a and d). Such difference in the position of cold center at 500h-hPa relates to the behavior of the rainband as pointed out in our original manuscript since it affects the warm advection as discussed below.
In the revised manuscript, we have deepened the analysis on warm advection at 500-hPa. The analysis was performed to elucidate which wind or temperature is attributable to intensify the advection. Here we use climatological fields of wind and temperature to test how each of them can modulate the temperature advection. The result (Fig. R2-8d) reveals that the anomalous wind is the main factor regulating the anomalous temperature advection in all clusters. The sign of the total temperature advection is, however, dependent on the magnitude of the cold advection induced by anomalous temperature. This temperature-induced cold advection is found to be controlled by the position and intensity of the cold center as part of the wave train in East Asia ( Fig. R2-8 b and e). This cold center exists in the lead days for all clusters but with different latitudinal position. In clusters 2 and 3 the cold center is located close to the wet band (35°N), while it is located north of the wet band in clusters 1 and 4 (Fig. R2-8b and e). These positioning of cold center nicely correspond to the pathway of the wave train, and thus reflect the jet structure in each cluster. Through above-mentioned mechanism, the ISM diabatic heating could modulate the East Asian rain band in a different manner in accordance with the jet structure.  HIST composite anomalies of (a) / at lag0 days, (b) lag 3-4 days precipitation (shading) and 200-hPa geopotential height (contours), and temporal evolution from lead 10 day to lag 10 day of zonal mean (c) precipitation (shading) and 500-hPa temperature advection (contours, unit: K/day), (d) 500-hPa temperature advection with climatological wind field (shading) and climatological temperature field (contours, unit: K/day), (e) 500-hPa meridional wind (shading) and temperature (contours, unit: K), and (f) 850-hPa meridional wind (shading) and vertical integrated water vapor flux (arrows), for clusters 1-4 (columns from left to right, respectively). The zonal mean is the average over 105°-140°E. The stippled regions denote anomalies significant at the 99% confidence level based on the two-sided Student's t test. Green hatching in (d) indicates vertical velocity (unit: Pa/s) lower than 0, significant at the 99% confidence level. "Nhist" indicates the number of samples used for the composite.
4.The study emphasized that the CGT mode presented here varies energetically on subseasonal time scales, using a lead-lag composite method covering data from day-10 to day+10. In the paper, it is better to further illustrate what the main periodicity of this feature is. A power spectrum analysis of the time series related to this subseasonal variability may help the authors answer this question. Response: Thank you for your interesting comments. We are very much interested in this issue. Hence, we conducted additional analysis on the periodicity of CGT. The procedures and results are explained as below.

i. Creating a CGT index
We first create a CGT reference pattern by applying EOF analysis to seasonal mean (June-September) meridional wind at 200-hPa (V200) over the domain of 20°-60°N, 0°-150°E, following the previous study (Yasui and Watanabe, 2010). The EOF1 patterns from JRA-55 and HIST resemble each other (Fig. R2-9). Hence, we used HIST EOF1 pattern in the following analysis for convenience. We then project the daily June V200 anomalies onto this reference pattern to obtain the daily CGT index (hereafter, CGTI-PC1) for both JRA-55 and HIST. The CGTI-PC1 represents the subseasonal variation of CGT wave pattern. We also compute another CGT index for comparison, which was introduced by Ding and Wang, (2005), by area-averaging the 200-hPa geopotential height over 35°-40°N, 60°-70°E (hereafter, CGTI-DW).

ii. Subseasonal variation of CGT indices
In order to track the variation of CGT associated with ISM activities, Fig. R2-10 shows mean CGTI variations from a lead of 20 day to a lag of 20 day. For both CGTI-PC1 and CGTI-DW, the composite mean variations show an "M" shape (i.e., peak-valley-peak), meaning strengthening CGT activities in pre-and post-lag0 days. Power spectrum analysis for these CGTIs shows a power around 0.06-0.08 (day -1 ) which roughly corresponds to 10-20 day (Fig. R2-10c  and d). Furthermore, the power spectrum for each event show there is a significant power (i.e., exceeding 95% red noise significance boundary) in the 5-25-day range (Fig. R2-10e), with a dominant mode of 0.07 day -1 (Fig. R2-10f). These results suggest that the CGT behaves as a iii. Mechanism of subseasonal variation of CGT Finally, the possible mechanism of the subseasonal CGT variation is discussed. The first peak of the "M" shape for the subseasonal CGT variation denotes the propagation of the CGT wave train from western Europe toward the ISM domain. At this stage, the East Asian CGT wave train is still weak (Fig. R2-11). On lag0 day, the vigorous ISM diabatic heating forces a wave train which becomes more pronounced over East Asia than in prior days. The wave train, however, is orthogonal (e.g., half-phase shifted) to CGT at lag0, as denoted by the valley of the "M" shape ( Fig. R2-10a and b). Few days later, the CGT emerges again but much more energetically in the East Asian region. Furthermore, the LBM output for June basic state well overlaps the wave train at lag10 day, suggesting the wave train induced by diabatic heating of ISM is developed and trapped in the westerlies, propagates in phase with CGT.
Overall, our additional analysis suggests that CGT wave train interacting with ISM diabatic heating exhibits a character of quasi-biweekly oscillations. The first phase is the propagation of wave train from western Europe, far upstream of the ISM domain. The second phase is the active of the ISM convection and diabatic heating which excites the wave train and maintains propagation to East Asia. Here, ISMH activities might be related to European-CGT wave train as illustrated by Ding and Wang, (2007Wang, ( , 2009). Finally, the ISM-induced wave train takes on a CGTlike pattern as it extends horizontally along the westerly jet. Very interestingly, the result at lag10 well overlaps the wave train simulated by LBM using June basic state (Fig. R2-11f). The transition of the wave pattern between lag0 and lag10 over East Asia is proposed to be linked to ISMH activities as similar transition pattern was seen in the LBM simulation (results omit). In the revised manuscript, we have added Fig. R2-11 as Fig. 2, and relevant description to Line 112-146. Figs. R2-9 and R2-10 is added as Figs. S8 and S4, respectively, to the supporting information.  Fig. R2-9). Dotted regions denote anomalies significant at the 90% and 99% confidence level for the observations and HIST, respectively.
5. Some minor wording issues pop up here or there in the current form. I prefer to place my main focus on these wording problems next round after these scientific issues I just raised could be ironed out.
Response: We made a thorough self-check of the text. We trust the readability has been improved and wording becomes more appropriate.
Reviewer #3 (Remarks to the Author): Comment on "East Asian summer rainfall stimulated by subseasonal Indian monsoonal heating" by Li et al.
The authors found that on the sub-seasonal timescales, an Indian anomalous diabatic heating may serve as a predictor for East Asian rainfall in early summer. The authors first identified the leading mode of June rainfall in East Asia on the interannual timescales and revealed the relation to Indian rainfall. Then, the authors proposed that the Indian diabatic heating anomaly can lead to East Asian rainfall variability through a wave train along the Asian westerly jet. Finally, the authors showed the different role of the Indian diabatic heating, depending on jet structures. The authors used a large ensemble simulation to support the results revealed based on the observations. The large number of samples made the result more robust and reliable. The results are interesting and may help to improve understanding and prediction of sub-seasonal variability of East Asian summer rainfall. However, I have a major concern about the logic connection between the three parts in the Results section. Response: We thank the reviewer for the positive evaluation of our study, and we also appreciate the reviewer's comments that help us improve the manuscript quality. We have revised the manuscript carefully according to the comments. The following is our point-by-point response.
First, I am a little confused about the connection between the results in sections 2.1 and 2.2. In section 2.1, the authors found a close relationship, on the interannual timescales, between the leading mode of East Asian rainfall in June and Indian rainfall. In section 2.2, the authors attempted to explain the interannual connection using the sub-seasonal variability of Indian diabatic heating, which is, however, not convincing. As described in Lines 151-152, for example, the dipole rainfall pattern in East Asia, which is the leading mode in June on the interannual timescales but is not prominent on the sub-seasonal timescales.
Response: In the previous version of the manuscript, we aimed to introduce a link between East Asian subseasonal rainfall variability and interannual variability to guide readers towards seasonal prediction. However, as suggested by the reviewer, we have decided to remove most of the interannual results in the revised manuscript as they are less relevant to the main objective of the study. As Fig. R3-1 (now Figure 6 in the revised manuscript) shows, stronger ISM forcing indicated by higher ISMH values corresponds to higher PC1 scores for East Asian precipitation (EAPR-PC1) at interannual timescale. This is nice evidence denoting that subseasonal activity of ISM heating could lead to modulation of interannual precipitation variability over East Asia. Description related to this figure has been added to discussion section (line 271-290) to underscore the potential significance of subseasonal ISM heating to interannual variation of East Asian rainband. Second, in section 2.3, the authors tried to analyze the interaction of CGT and wet band in East Asia using cluster analysis based on jet structure, and proposed the Indian diabatic heating anomaly can serve as a trigger or a reinforcer for wet band depending on if the warm advection is preexisting. What is the relationship between the jet structure and the preexisted warm advection? Response: Thank you for your constructive comments. We first invite the reviewer to our revised manuscript for some updated results. To be concise, we claim that the ISM diabatic heating has an effect to maintain the CGT wave train that propagates toward East Asia. This feature is confirmed by two additional analyses: composite analysis and linear baroclinic model experiment (Figure 2 and Line 125-136 in the revised manuscript). As we propose in the revised manuscript, southerly wind over East Asia induced by ISM heating is important because it promotes midtropospheric warm advection near the wet band and fuels the low-level moisture to the wet band.
The leading pattern for summer East Asian jet variability is known as the meridional displacement of the Asian jet (JMD) (e.g., . In the clusters, we have observed that the East Asian jets exhibit JMD-related characteristics. Specifically, in clusters 1 and 4, the north JMD is observed as the East Asian jets tend to run further north, whereas in clusters 2 and 3, the south JMD is observed as the East Asian jets tend to travel further south (Fig. R3-2). The north and south JMD are also related to meridional wind anomalies; specifically, the former corresponds to tropospheric southerly winds prevailing in East Asia (i.e., Figs. R3-4e and f in lead days), which is associated to warm advection together with enhanced moisture supply near the wet band, while the latter does not show such feature (Fig.  R3-2 bottom). These results are in consistent with previous studies about East Asian JMD (e.g., , Wang et al., 2019. In clusters 1 and 4, such beneficial conditions allow for the formation of the wet band in lead days, and it pre-exists. In contrast, for clusters 2 and 3, the wet band has not yet formed during the lead days because the southerly wind is not dominant in East Asia.
We have incorporated above discussions as text near line 161-164 and 209-211 in the revised manuscript. How does the jet structure affect different wave propagation illustrated in Figure 7, and how does the jet structure determine if the Indian diabatic heating is a trigger or a reinforcer for wet band in East Asia? The result of the different role of Indian diabatic heating, depending on jet structure, in wet band in East Asia is interesting, but the mechanisms need further clarification. Response: Thank you for your constructive comments. Our reply for each of your question is arranged below. i. How does the jet structure affect different wave propagation?
As we proposed in the previous comment, the major difference among clusters is the JMD. Clusters 1 and 4 are related north JMD (Figs. R3-2a and d), while the south JMD is found in clusters 2 and 3 over the East Asian domain (Figs. R3-2b and c).
Our claim is that the response of downstream atmospheric circulation over East Asia to upstream ISM heating is different according to the pathway of the jet stream. The vigorous ISM diabatic heating at lag0 perturbs the air close to the westerly jet in June and plays a role as Rossby wave source. Since westerly jet stream can act as a waveguide (Hoskins and Ambrizzi, 1993), it could confine the wave and direct it toward East Asia. Our results depict that clusters 2 and 3 show southward bent of the jet over East Asia (i.e., south JMD), hence, the wave train is heading to the south, forming a cold anomaly (negative geopotential height anomaly) center over East Asian sector on lag0 (Fig. R3-3 b and c). The cold center is consequently positioned southward of the jet (south of 35°N). In contrast, clusters 1 and 4 represent the jet running straight (namely a north JMD scenario), and thus, the wave tends to propagate zonally toward East Asia. This condition leads to the position of the cold center to be more north over the East Asian sector (Figs. R3-3a and d). Such difference in the position of cold center at 500h-hPa relates to the behavior of the rainband since it affects the warm advection as discussed in the next (please see our reply in the section ii in the next).
In the revised manuscript, Fig. R3-3 was adopted as Fig. S5, and relevant description were added in line 184-194 in the revised manuscript.
ii. how does the jet structure determine if the Indian diabatic heating is a trigger or a reinforcer for wet band in East Asia? As above, the jet structure determines the position of cold anomalies over East Asia. Here, we explain how it potentially relates to trigger/reinforcer role of ISH. To achieve that, we have deepened the analysis on warm advection at 500-hPa in the revised manuscript. We use climatological fields of wind and temperature to test how each of them can contribute the temperature advection. The result (Fig. R3-4d) reveals that the anomalous wind is the main factor regulating the anomalous temperature advection in all clusters. The sign of the advection is, however, dependent on the location and magnitude of the cold advection induced by anomalous temperature. Namely, this temperature-induced cold advection is found to be controlled by the position and intensity of the cold center in East Asia as part of the wave train (Figs. R3-4 b and e). This cold center exists in the lead days for all clusters but with different latitudinal position. In clusters 2 and 3 the cold center is located close to the wet band (35°N), while it is located north of the wet band in clusters 1 and 4 (Figs. R3-4b and e). These positionings of cold center nicely correspond to the pathway of the wave train, and thus reflect the role of jet structure in each cluster. Through above-mentioned mechanism, the ISM diabatic heating could modulate the East Asian rainband in a different manner in accordance with the jet structure.
In conclusion, we found that the jet pattern in the preconditions (i.e., event0) is important. It first determines the large-scale temperature advection in the lead days. Moreover, the jet structure guides the ISM-induced CGT wave train and determines the location of cold center over East Asian sector. Under south JMD condition (i.e., southward bending jet; clusters 2 and 3), the cold anomaly hinders the prevalence of warm advection in East Asia, could result in the ISM acting as the trigger. Our clustering results (together sections i and ii) suggest jet structure is important in HIST composite anomalies of (a) / at lag0 days, (b) lag 3-4 days precipitation (shading) and 200-hPa geopotential height (contours), and temporal evolution from lead 10 day to lag 10 day of zonal mean (c) precipitation (shading) and 500-hPa temperature advection (contours, unit: K/day), (d) 500-hPa horizontal temperature advection (shading) and meridional temperature advection (contours, unit: K/day), (e) 500-hPa meridional wind (shading) and temperature (contours, unit: K), and (f) 850-hPa meridional wind (shading) and vertical integrated water vapor flux (arrows), for clusters 1-4 (columns from left to right, respectively). The zonal mean is the average over 105°-140°E. The stippled regions denote anomalies significant at the 99% confidence level based on the two-sided Student's t test. Green hatching in (d) indicates vertical velocity (unit: Pa/s) lower than 0, significant at the 99% confidence level. "Nhist" indicates the number of samples used for the composite.

Minor comments
In section 2.2, the authors used the Q1 to represent the Indian summer monsoon heating. Why not use rainfall, which is more direct and maybe more accurate because the Q1 is calculated as the residual based on the thermodynamic equation? Response: Thank you for your comments. We carefully compared the relationship between precipitation anomaly index (PRI) and ISMH. The correlation coefficients between June daily Indian PRI (area-averaged over 10°-30°N, 70°-85°E) and daily ISMH (same domain as PRI but with mass-weighted integration from 850 to 200 hPa) are 0.80 and 0.99 (both with p<0.01), respectively, for APHRODITE PRI versus JRA-55 ISMH and HIST PRI versus HIST ISMH. This result indicates ISMH is a good proxy of precipitation in ISM domain. The ISMH we defined is a diagnosis capable to represent the gradual evolution of the ISMH corresponding to large-scale circulation. Furthermore, the ISMH is a substitute for precipitation index when in situ observation is lacking (e.g., over oceans or stations are sparse), in particular considering the analysis period of this study spanning from 1958-2020. We have added a brief description to line 381-388 (in method section).
What process is related to the dry band in East Asia since it is not correlated with the CGT wave pattern, Indian diabatic heating, and even rainfall in the western North Pacific (Fig. 6)? In addition, what is the relationship between the wet and dry band? Response: We are also interested in this question. The relationship among wet band, dry band, and ISMH are illustrated in Fig. R3-5, by computing lead-lag correlation of dry band PRI with respective to wet band PRI and ISMH. The results (both observation and HIST) indicate the dry band and wet band are likely to occur simultaneously, we therefore speculate that the development of wet band and dry band might reflect a northward location/migration of the rand band. Figure R3-5 also denotes that the dry band tends to be more prominent before the ISM heating becomes vigorous. This may be related to the activities of low-level easterly before the ISM to be active ( Fig. R3-4f; lower left corner for each panel). We have added this argument to line 306-307 in the revised manuscript.
The authors showed that the dipole rainfall pattern in East Asia can exist without Indian diabatic heating (Fig. S6). So, how much can the Indian diabatic heating account for the dipole rainfall pattern variability? Response: We attempted to estimate this ratio based on HIST data to sample more cases. The procedures are as followings. We first calculated the daily-basis EAPR-PC1 score as a projection of June subseasonal precipitation anomalies onto the interannual June EOF1 dipole pattern mode (Fig. S9 in the revised supporting information). Then, we counted the number of days after lag0 with projected PC1 > 0.5 (21,852 days). Among those, days with high PC1 are assumed relevant to the ISM diabatic heating since they occurred after the lag0. Meanwhile, the total days with PC1 > 0.5 are 77,494 days. Therefore, this ratio is about 28.20%. In conclusion, ISM heating may link to more than one fourth of the subseasonal dipole events in June. While we have made our best attempt, it is still a rough estimation. We therefore decide not to add this result to the main text; instead, we added few words (line 280-281 in revised manuscript), based on the EAPR-PC1 score of lag0 composite (Fig. R3-1h), to emphasize the possibility that the interannual variability of the dipole pattern can be explained by the subseasonal ISM heating.  L195: How does the anticyclonic anomaly cause the warm advection? Which component plays a dominant role, for example, the anomalous temperature advected by the climatological zonal/meridional winds or the climatological temperature advected by the anomalous zonal/meridional winds? Response: Thank you for your constructive comments., and we believe we have addressed the temperature advection related questions in our reply to the reviewer's major comment#2 (Fig.  R3-4f).
L206-208: what do you mean "the jet stream, which can also drive mechanically induced temperature advection that influences precipitation over East Asia"? Is the jet-drive mechanism different from the warm advection due to the anticyclone? If yes, what is the jet-drive mechanism? Response: We have deleted this sentence to avoid confusion. We believe we have addressed this question in reviewer's major concern#1 and #2 (Figs. R3-2, R3-3, and R3-4d). manuscript). In these clusters, the vertical structure of zonal wind anomalies displays a typical northward JMD, with corresponding north-positive and south-negative zonal wind anomalies extending from the upper troposphere to near surface (Figs. R3-4a and d). The northward JMD accompanies the strong southerly wind (e.g., , as we also observed in clusters 1 and 4 whose southerly winds prevail in the troposphere (Figs. R3-4a and d). It also shows the region with strong southerly winds coincides with anomalous temperature advection (Figs. R3-4a and d). In contrast, clusters 2 and 3 exhibit a southward bending jet flow (Fig. 3 in the original manuscript), and we found reversed tropospheric zonal wind anomalies compared to clusters 1 and 4, indicating a southward JMD feature (Figs. R3-4b and c). In these clusters, the meridional wind and temperature advection anomalies in the troposphere are not clearly seen near the wet band area.
In conclusion, the major difference about the East Asian jet pattern over prior to the extreme ISM heating between clusters 1 and 4 and clusters 2 and 3 is the jet meridional displacement. The jet displacement to north as in clusters 1 and 4 leads to warm advection sustained by southerly wind.
Furthermore, we found that daily wet band precipitation in June is evidently correlated with both the 500-hPa meridional wind and the northward JMD (Figs. R3-5a, b and c). The subseasonal lead-lag correlations between them, however, indicate that the southerly wind occurred prior than wet band precipitation, and the JMD is likely to occur later than the appearance of both the southerly wind and wet band (Figs. R3-5d). Therefore, the jet structure with northward JMD is possibly a sign for the existed southerly wind and wet band, which lead to the upcoming ISM forcing as a reinforcer, and vice versa as a trigger for clusters 2 and 3.
The  showing the anomalous wind field of 4 clusters on event0 days. The color shading represents zonal wind anomalies, the arrows indicate meridional wind with vertical velocity, and the black and red contours depict reconstructed wind patterns and temperature advection anomalies, respectively. Stippled regions and arrows indicate anomalies that are statistically significant at the 99% confidence level. Fig R3-5. The spatial pattern of HIST June standardized daily area-mean wet band precipitation (Npr) regression against (a) precipitation, and 500-hPa (b) meridional wind (V500), and (c) zonal wind (U500). (d) June subseasonal lead-lag correlations among Npr, area-averaged 500-hPa meridional and zonal winds near the wet band. The V500 lead-lag Npr means lead-lag correlations of 500-hPa meridional wind with respective to Npr, similarly for others. Values with solid circle satisfy p<0.01. The green rectangle denotes the wet band area, similar to Supplementary Fig. 1.